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Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published: June 10th, 2021



1Department of Health Sciences, University of Burgos, 2Department of Geography, History and Communication, University of Burgos, 3Department of Computer Engineering, University of Burgos, 4tvUBU, University of Burgos, 5Departament of Philology, Universidad de Burgos

We present a protocol for a behavioral analysis of adults (ages 18 to 70-year-old) engaged in learning processes, undertaking tasks designed for Self-Regulated Learning (SRL). The participants, university teachers and students, and adults from the University of Experience, were monitored with eye-tracking devices and the data were analyzed with data-mining techniques.

Behavioral analysis of adults engaged in learning tasks is a major challenge in the field of adult education. Nowadays, in a world of continuous technological changes and scientific advances, there is a need for life-long learning and education within both formal and non-formal educational environments. In response to this challenge, the use of eye-tracking technology and data-mining techniques, respectively, for supervised (mainly prediction) and unsupervised (specifically cluster analysis) learning, provide methods for the detection of forms of learning among users and/or the classification of their learning styles. In this study, a protocol is proposed for the study of learning styles among adults with and without previous knowledge at different ages (18 to 69-year-old) and at different points throughout the learning process (start and end). Statistical analysis-of-variance techniques mean that differences may be detected between the participants by type of learner and previous knowledge of the task. Likewise, the use of unsupervised learning clustering techniques throws light on similar forms of learning among the participants across different groups. All these data will facilitate personalized proposals from the teacher for the presentation of each task at different points in the chain of information processing. It will likewise be easier for the teacher to adapt teaching materials to the learning needs of each student or group of students with similar characteristics.

Eye-tracking methodology applied to behavioral analysis in learning
Eye-tracking methodology, among other functional uses, is applied to the study of human behavior, specifically during task resolution. This technique facilitates monitoring and analysis during the completion of learning tasks1. Specifically, the attention levels of students at different points of the learning process (start, development, and end) in different subjects (History, Mathematics, Science, etc.) can be studied with the use of eye-tracking technology. In addition, if the task includes the use of videos with a voice that guides the learning proces....

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This protocol was performed in compliance with the procedural regulations of the Bioethical Committee of the University of Burgos (Spain) nº Nº IR27/2019. Prior to their participation, the participants had been made fully aware of the research objectives and had all provided their informed consent. They received no financial compensation for their participation.

1. Participant recruitment

  1. Recruit participants from among a group of adults within two environments (students .......

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The 36 participants recruited for the present study were from three groups of adults (students from the university of experience, university professors, and undergraduate and master's degree students) with ages ranging between [18 and 69] years (Table 2). The protocol was tested over 20 months at the University of Burgos. An outline of the development can be seen in Table 4.


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The research results indicated that the average fixation duration on the relevant stimuli was longer among participants with previous knowledge. Likewise, the focus of attention on this group is on the middle points of information (proximal and distal)7. The results of this study have revealed differences in the way participants processed the information. Furthermore, their processing was not always linked to the initial grouping (University of Experience Students, University Teachers and Graduate.......

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The work has been developed within the Project "Self-Regulated Learning in SmartArt Erasmus+ Adult Education" 2019-1-ES01-KA204-095615-Coordinator 6, funded by the European Commission. The video of the task completion phase had the prior informed consent of Rut Velasco Sáiz. We appreciate the participation of teachers and students in the task implementation phase.


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